Day 1: Solid-State Battery Fundamentals & Materials Data Preparation
- Fundamentals of lithium-ion and solid-state batteries
- Solid electrolytes, ionic conductivity, stability, and key performance factors
- Introduction to AI and materials informatics in battery research
- Materials Project and OQMD for battery-material data
- Material structure processing using Pymatgen
- Feature and descriptor generation using Matminer
- Building an AI-ready solid-electrolyte dataset
- Basic visualization and screening of candidate materials
Hands-On: Create and explore an AI-ready solid-electrolyte materials dataset.
Day 2: Machine Learning & Explainable AI for Materials Discovery
- Data preprocessing and feature selection
- Ionic-conductivity and materials-property prediction
- Random Forest and XGBoost modeling
- Model validation using MAE, RMSE, R², and cross-validation
- Predicted vs actual performance analysis
- Explainable AI using SHAP
- Identification of important material descriptors
- AI-based ranking of promising solid-electrolyte candidates
- Introduction to physics-informed machine learning for battery research
Hands-On: Build, evaluate, and interpret an explainable AI model for solid-electrolyte screening.
Day 3: Battery Health, SOH & Remaining Useful Life Prediction
- Introduction to battery cycling and degradation datasets
- Capacity fade and battery-aging analysis
- Feature engineering from voltage, current, capacity, temperature, and cycle data
- State of Health (SOH) estimation and prediction
- Baseline ML models for battery-health prediction
- Time-series modeling using LSTM
- Battery degradation forecasting
- Remaining Useful Life (RUL) estimation
- Comparison of machine-learning and deep-learning models
- Research interpretation and identification of future experimental directions
Hands-On: Develop an AI workflow for SOH prediction, degradation forecasting, and RUL estimation.
